1. Field of the Invention
The present invention relates generally to fluorescence correlation spectroscopy (FCS). Particularly, the present invention relates to improved data analysis of photon arrival and photon count data typically supplied to FCS analysis by utilizing Photon Arrival time Interval Distribution (PAID) analysis techniques.
2. Description of Related Art
The publications and other reference materials referred to herein to describe the background of the invention and to provide additional detail regarding its practice are hereby incorporated by reference. For convenience, the reference materials are numerically referenced and grouped in the appended bibliography. The contents of these publications and other reference materials are hereby incorporated by reference.
With the availability of the complete sequences of genomes of several organisms, it is critical to determine the biological function of the proteins coded by those genomes. Analysis of protein-protein interactions is important for this process since it can produce protein-protein interaction maps that place each protein in its cellular context, from which it is hoped to infer the protein's function. [1, 2]. Several existing methods that monitor protein-protein interactions are: conventional and modified yeast two-hybrid systems along with reconstitution systems, phage display, fluorescence resonance energy transfer (FRET) methods, mass spectrometry, protein chips, and evanescent wave methods [1-5].
Fluorescence correlation spectroscopy (FCS) and related single-molecule methods are important tools for the in vitro analysis of macromolecular interactions, and are potentially useful for in vivo analysis [6, 7]. FCS-related methods can detect these interactions in a distance-independent fashion, unlike FRET. [8]. FCS-related methods detect macromolecular interactions by monitoring fluorescence fluctuations that result when fluorescent molecules diffuse or flow across a tightly focused laser excitation volume (femtoliter confocal detection volume). At concentrations less than 1 nM, the average molecular occupancy of the detection volume is smaller than one, allowing the detection of photon-bursts generated by single molecules. This is commonly referred to as the “low occupancy” regime. A “photon burst” is the set of all photons detected from a single molecule during its transit through the confocal detection volume. Analysis of these photon-bursts has been used to measure the distribution of molecular properties, such as fluorescence lifetime, polarization anisotropy, and fluorescence resonance energy transfer (FRET) [9-12]. At concentrations between 1 nM-100 nM, the molecular occupancy is still low enough to be sensitive to the addition or subtraction of one molecule within that volume. This is referred to as the “intermediate occupancy” regime. Although it is not possible to separate the photons into bursts from single molecules, the resulting fluctuating fluorescence signal contains dynamic information about several processes such as translational diffusion [13], rotational diffusion [14], intersystem crossing to triplet states [15], and photobleaching [16]. At concentrations greater than 100 nM, many molecules occupy the detection volume and the fluctuations are averaged out. This is known as the “high occupancy” regime. The primary drawback of using FCS-related methods for monitoring macromolecular interactions is the dynamic range over which binding can be detected. These methods are most sensitive in the nM concentration regime, whereas binding constants of protein-protein interactions often correspond to higher concentrations (this limitation is partially offset by the ability of FCS-related methods to detect small subpopulations).
Fluorescence bursts or fluctuations are ideally suited to the study of macromolecular interactions. FIG. 1 shows how the properties of the sample translate into features of the fluorescence signal for a detection volume with low occupancy. Macromolecular interactions such as homo-dimerization and aggregation can be measured using single-channel methods. FIG. 1A depicts a single-channel measurement on a sample containing a mixture of monomers carrying one yellow fluorescent label and tetramers carrying four yellow fluorescent labels in solution. The laser excitation profile (shown in green) and the detection pinhole define the effective detection volume. As these molecules diffuse in and out of the laser excitation profile, bursts of fluorescence photons are detected, shown as an intensity time trace to the right. The three basic characteristics of a single-channel photon burst are: (1) the brightness of the bursts (blue arrows), which is proportional to the number of fluorescence labels detected, (2) the duration of the burst (red arrows), which is related to the diffusion time of the molecule across the laser beam, and (3) the time between bursts of the same species (green arrows) which is inversely proportional to the concentration of that species. The same characteristics apply for the fluctuation analysis used at higher concentrations (intermediate occupancy). In general, the fluorescence signal from an interacting pair of molecules or an aggregate has different characteristics than that from a free single molecule. The complex or aggregate has a larger hydrodynamic radius, which results in a longer diffusion time. It also has more labels than free molecules, which results in increased brightness of the bursts (fluorescence quenching and incomplete labeling are ignored at this stage). To most effectively detect binding or aggregation, a data analysis scheme that measures all these properties at the same needs to be developed.
For interactions between macromolecules of different types (for example hetero-dimerization of two proteins), extending the analysis to two channels improves the sensitivity over one channel analysis [8]. The molecules of one type are labeled with one color (eg. yellow), and the molecules of the other type are labeled with another color (eg. red.) A complex of the two types of molecules has both labels. This is the situation shown to the left of FIG. 1B. Signal from the two fluorophores is separated spectrally onto two detector channels, yellow and red. In addition to the ways described for the single-channel case, the binding of two molecules labeled with the yellow and red fluorophores can be indicated by the detection of simultaneous photon bursts on both channels (orange arrow in FIG. 1B). This coincident detection indicates that both fluorescence labels are present, and thus the two molecules are bound.
Single-channel Data Reduction and Analysis: The task of the data analysis performed on these fluorescence signals is to extract these parameters using all of the information possible, and to present an interpretable graphical representation of the data that summarizes the relevant information in the data set. Several methods summarized below have been developed to perform this task. All of these methods are able to handle vast amounts of data by “reducing” the data to a one- or multi-dimensional histogram, from which the information on the diffusing species is extracted. The trick is to reduce the data, but not too much. As much information as practically possible should be retained to characterize the sample.
Fluorescence Correlation Spectroscopy (FCS): FCS analyzes fluorescence fluctuations through the use of the correlation function [13, 17, 18]. Correlation functions calculated from the intensity signal reveal the time scale and amplitude of various molecular processes, but do not reveal the brightness of each source. In single-channel applications, macromolecular interactions can be detected by monitoring the change in diffusion time resulting from the interaction of two molecules. However, binding often does not produce a large change in diffusion time: for a sphere, doubling the hydrodynamic volume (for instance by binding two equally sized subunits) produces only a 26% increase in diffusion time (since the diffusion time scales with the hydrodynamic radius, which roughly scales as [molecular weight]1/3). Therefore, a large change in size is required to measure binding using diffusion constants. A further complication is that the shape of the bound molecules is also important; there is no general relationship between diffusion time and the number of subunits. For example, a short, rod-like dsDNA fragment will diffuse more slowly than a globular protein with the same volume (compare the diffusion constant calculations for a sphere with those for a rod in [19]).
Brightness is a reporter of binding events (ignored by FCS) that can in fact be more sensitive than the diffusion time. If two interacting macromolecules are both labeled, the brightness of the interacting complex is double the brightness of the individual subunits, provided that the quantum yield does not change (which is not always the case.) Brightness has the advantage that the shape of the molecule does not affect it, unlike the diffusion time. Several methods have been developed to use this information.
Moment Analysis of Fluorescence Intensity Distribution (MAFID) and Higher order correlation amplitudes: Moments of the photon counting histogram can be used to monitor occupancy and brightness of labeled macromolecules [20, 21]. By comparing the values of the mean (first order moment), the variance (second order moment), and the third order moment, values for the occupancy and brightness of two species can be extracted. In this way, macromolecular interactions can be monitored by taking advantage of the change in molecular brightness when labeled molecules interact. Another method discussed in [22] uses the amplitudes of higher order correlations to extract the occupancy and brightness, but turns out to be equivalent.
Photon Counting Histogram (PCH) and Fluorescence Intensity Distribution Analysis (FIDA): Rather than calculating the moments of the photon counting histogram as described above, it is possible to fit the histogram directly, thereby using more information to extract brightness and occupancy [23, 24]. In this way, sub-populations with different brightness can be separated [25]. The PCH and FIDA methods differ mainly in their treatment of the shape of the detection volume. PCH has been used to monitor ligand-protein binding equlibria [26], to probe the stoichiometry of protein complexes [27], and to study oligonucleotide-polymer interactions [28]. FIDA has been used to probe receptor-ligand interactions in a format compatible with ultra-high throughput screening [29, 30].
The PCH and FIDA methods contain information about the brightness and occupancy of fluorescent species, but lack the information on dynamics contained in the correlation function. For a sample with a single species, it is possible to perform FCS and PCH or FIDA on the same data set to extract both the brightness and diffusion time [31]. However, if there are multiple species, each with a different diffusion time and brightness, there is no direct way to relate each diffusion time found to its corresponding brightness. A method that simultaneously tracks diffusion time and brightness is necessary to address such heterogeneous samples.
Fluorescence Intensity Multiple Distribution Analysis (FIMDA): By using a series of photon count histograms with multiple time bin widths, it is possible to obtain the same temporal information as FCS while gaining the information on brightness [32, 79]. This is because the shape of the photon count histogram is affected by the fluctuations that occur on the time scale of the time bin width. This method is termed Fluorescence Intensity Multiple Distribution Analysis (FIMDA). Macromolecular interactions can be tracked using FIMDA by monitoring brightness and diffusion time simultaneously.
The type of information that is available from each of the previously described methods is as follows: FCS extracts concentration and diffusion time (and other temporal dynamics), but not brightness; MAFID, PCH, and FIDA extract concentration and brightness, but not diffusion time; and FIMDA extracts concentration, brightness, and diffusion time (and other temporal dynamics).
Multiple-channel Data Reduction and Analysis: Coincident detection of two fluorophores of different colors is a more sensitive indicator of binding events than brightness or diffusion time used in single-channel studies [8]. This is because: (1) coincident bursts are only detected when two molecules are associated, (2) it is less sensitive to quenching of fluorescence, and (3) coincident detection in two channels benefits from the properties of ratiometric measurement. If two interacting molecules are labeled with the same fluorophore, it is necessary to detect distinct subpopulations with a factor of 2 difference in brightness. If they are instead labeled with different-color fluorophores, the experiment is reduced to a simple “yes or no” question. A signal in each channel indicates the presence of the corresponding species. A simultaneous signal in both channels indicates a complex (1:1 ratio between channels), and a signal on only one channel indicates a free molecule (1:0 or 0:1 ratio between channels; random coincidence of signals also needs to be taken into account). The benefit of ratiometric measurement is described in the following. If a fluorescent molecule traverses the same path through the detection volume many times (ignoring triplet-state-induced fluctuations), the number of photons detected from the molecule during those traversals would follow a Poisson distribution, characterized by a mean number of photons (this noise, which is inherent in photon counting experiments, is referred to as “shot noise”). This mean number of photons depends on the path the molecule takes through the detection volume. Taking into account all possible paths through the detection volume, the distribution in photon counts is considerably widened in comparison to shot noise. In contrast, the ratio between the intensity of two channels for an isolated burst is less affected since the mean value of this ratio does not depend on the path taken through the detection volume (the width of the distribution in ratios, however, does depend on the path of the molecule). Measurements using the ratio between two channels (or that consider joint distributions for the two channels) reduce the noise due to differing paths through the detection volume, and are thus more sensitive [11].
Dual-color Cross-correlation FCS: In dual-color cross-correlation FCS, interactions between molecules labeled with two different colors are monitored using the cross-correlation amplitude [8, 33-35]. Significant correlation amplitudes result only when a diffusing species contributes to both channels. By choosing different-color fluorophores that can be separated into different channels with minimal leakage and characterizing the background, it is possible to read the occupancy of bound molecules directly as the amplitude of the cross-correlation. As with single-channel FCS, the diffusion time of the complex can be extracted. The occupancy and diffusion times of the free components can also be extracted by analyzing the autocorrelation of each channel. However, it is necessary to measure using a different method or to assume values for the relative brightness of the different species in order to extract the occupancies and diffusion times.
Ratiometric single-molecule methods [multi-parameter fluorescence detection (MFD), two-dimensional fluorescence intensity distribution analysis (2D-FIDA), single-pair Fluorescence Resonance Energy Transfer (spFRET)]: Photon burst analysis based on ratiometric methods has been developed for monitoring FRET, polarization anisotropy, and spectral fluctuations [11, 36]. If the distance between two labeled molecules is in the 2-8 nm range, FRET can be used to monitor the interaction. For example, single-pair FRET has been used to monitor the cleavage of DNA by a restriction enzyme in solution [12]. The same ratiometric data analysis can also be applied to macromolecular interactions where the separation between fluorophores is greater than the 20-80 Å nm range for FRET, although this has not been done. In this case, it is necessary to excite both fluorophores individually and perform coincidence detection.
Multi-parameter fluorescence detection (MFD) and 2D-FIDA perform tasks similar to the ratiometric single-molecule methods, with additional capabilities. MFD has the additional ability to measure fluorescence lifetime [9, 37, 38], and can also be used to obtain the brightness information available with PCH and FIDA [10]. Originally, the single-molecule measurements with fluorescence lifetime were performed with a single detector, although now they have been extended to multiple channels. 2D-FIDA is the extension of the single-channel FIDA method described above to two channels. In extracting the occupancy and brightness in each channel, it takes advantage of both the ratiometric and brightness information. It can be used for samples in the low and intermediate occupancy regimes [39].
The type of information that is available from each of the previously described multiple-channels method is as follows: Cross-correlation FCS extracts coincidence, concentration and diffusion time (and other temporal dynamics), but not brightness; MFD can detect coincidence and extract brightness and ratiometric information for multiple channels, and diffusion time, however, it can only work with low occupancy samples; and 2D-FIDA can detect coincidence, and extract brightness and ratiometric information for multiple channels. It can work with low and intermediate occupancy samples, but does not extract diffusion time (or other temporal dynamics).
What is lacking in the existing methods is a way to combine the dynamic information available using cross-correlation FCS with the ratiometric and brightness information available with MFD, 2D-FIDA, and ratiometric single-molecule methods, while allowing analysis to be performed at concentrations corresponding to low and intermediate occupancies.